Computer vision-based method for online measuring the moisture of iron ore green pellets in disc pelletizer

Zhou, Shuyi; Liu, Xiaoyan

doi:10.1109/cac53003.2021.9728151

Cited by 3 publications

(1 citation statement)

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“…Another method used to determine the degree of moisture in a material is image processing. The literature presents applications of vision methods to assess the moisture of soil [ 31 , 32 ], milled rice [ 33 ], loess [ 34 ], coal [ 35 ], and iron ore [ 36 , 37 ]. In [ 31 ], the images were processed to determine the median values in the red, green, and blue bands, as well as the hue, saturation, and value of the HSV color space.…”

Section: Related Workmentioning

confidence: 99%

Moisture Determination for Fine-Sized Copper Ore by Computer Vision and Thermovision Methods

Buchczik

Budzan

Krauze

et al. 2023

Sensors

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The moisture of bulk material has a significant impact on the energetic efficiency of dry grinding, resultant particle size distribution and particle shape, and conditions of powder transport. This research aims to develop computer vision and thermovision techniques for the on-site estimation of moisture content in copper ore, for use, e.g., in dry grinding installations. The influence of particle size on the results of moisture estimation is also studied. The tested granular material was copper ore of particle size 0–2 mm and relative moisture content of 0.5–11%. Both vision and thermovision images were taken at standard and macro scales. The results suggest that median-intensity vision images monotonically reflect copper ore moisture in the range of about 0.5–5%. Suitable models were identified and cross-validated here. In contrary, thermograms should not be analyzed simply for their mean temperature but treated with computer vision processing algorithms.

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